Abstract
The Normalized Difference Vegetation Index (NDVI) is widely used to monitor vegetation phenology and productivity around the world. Over the last few decades, phenology monitoring at large scales has been possible due to the information and metrics derived from satellite sensors such as the Moderate Resolution Imaging Spectroradiometer (MODIS) or the Project for On-Board Autonomy–Vegetation (PROBA-V). However, due to their temporal and spatial resolution, adequate ground comparison is lacking. In this paper, we analyze how NDVI products from MODIS (Aqua and Terra) and PROBA-V predict vegetation phenology when compared with near-surface observations. We conduct this comparison at four tropical dry forests (TDFs) in the Americas. We undertake this study by comparing the following: (i) Dissimilarities of the standardized NDVI (NDVIS) using dynamic time warping, (ii) the differences of daily NDVIS between seasons and ENSO months using generalized linear models, and (iii) phenometrics derived from NDVI time series. Overall, our results suggest that NDVIS from satellite observations present DTW distances (dissimilarities) between 2.98 and 46.57 (18.91 ± 12.31) when compared with near-surface observations. Furthermore, NDVIS comparisons reveal that overall differences between satellite and near-surface observations are close to zero, but this tends to differ between seasons or when El Nino Southern Oscillation (ENSO) is present. Phenometrics comparisons show that metrics derived from satellite observations such as green-up, maturity, and start and end of the wet season strongly correlate with those from near-surface observations. In contrast, phenometrics that describe the day of the highest or lowest NDVI tend to be inconsistent with those from near-surface observations. All findings were observed independently of the NDVI source. Our results suggest that satellite-based NDVI products tend to be inconsistent descriptors of vegetation events on tropical deciduous forests in comparison with near-surface observations. These results reinforce the idea that satellite-based NDVI products should be used and interpreted with great caution and only in ecosystems with well-established knowledge of their vegetation phenology.
Highlights
Monitoring vegetation phenology is fundamental to the inference of biochemical cycles and their relationship to environmental conditions and stresses
It seems that the differences in the Normalized Difference Vegetation Index (NDVI) magnitude between near-surface and satellite observations tend to be more pronounced at higher latitudes (e.g., Chamela Biological Station (CBS), Lagoa do Cajueiro State Park (LC-SP), or PEMS-EMSS; Figure 2a,c,e,f,g) than close to the Equator (e.g., SRNP-EMSS; Figure 2b)
Our results reveal differences in the temporal variations of satellite-based NDVI products from Moderate Resolution Imaging Spectroradiometer (MODIS) and Project for On-Board Autonomy–Vegetation (PROBA-V) in comparison with those from high temporal resolution optical phenology towers
Summary
Monitoring vegetation phenology is fundamental to the inference of biochemical cycles and their relationship to environmental conditions and stresses. Vegetation phenology has been monitored at large scale using earth observation approaches and their derived vegetation indices [3,4] Current satellite missions, such as Moderate Resolution Imaging Spectroradiometer (MODIS) from the National Aeronautics and Space Administration or Project for On-Board Autonomy–Vegetation (PROBA-V) from the European Spatial Agency provide vegetation indices with the potential to describe the vegetation dynamics on a given landscape at reasonable spatial and temporal resolutions. Products such as the Normalized Difference Vegetation Index (NDVI) have been widely used as indicators of vegetation phenology over the last few decades at the local, regional, and global levels [4,5,6,7]. The use of NDVI products must be carefully considered since these observations could be affected by a large number of factors (e.g., variations in solar zenith and viewing angles, atmospheric conditions, topography, cloud cover, surface reflectance bidirectional effects, and leaf area index, among many) [14,15,16]
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